Machine Learning Engineer
$160K–$200K+ Offers For Graduates $160K–$200K+ Offers For Graduates 

3 weeks remote, 7 weeks onsite in Austin, TX
80–100 hours/week for 10 weeks
In-person
Short-term contract
full-time (90 hrs/week)

Machine Learning Engineer   $160K–$200K+ Offers For Graduates $160K–$200K+ Offers For Graduates 

Description

Engineers who claim to build meaningful systems rarely get the opportunity to prove it. This is that opportunity: consistent deployment cycles, rigorous assessment, and operational AI infrastructure that influences how the federal government functions. No résumé posturing. No abstract problem sets. Only tangible production work, delivered weekly, in high-pressure conditions.

Gauntlet for America is a fully funded, competitive 10-week fellowship that builds AI-native engineering capacity for the United States government. It serves as a high-stakes proving environment for seasoned engineers ready to demonstrate their ability to construct and maintain production-quality AI systems where reliability, security, and measurable impact are non-negotiable.

Fellows deliver weekly shipments, work under continuous evaluation, and train in cohorts with other top-tier engineers. Graduates who complete the program successfully transition into federal GS-12 engineering positions (~$150K + comprehensive federal benefits), contributing to systems that directly affect governmental operations.

The fellowship spans 10 weeks: 3 weeks conducted remotely, then 7 weeks onsite in Austin, Texas. Participants should anticipate a demanding schedule (80–100 hours/week) structured to accelerate skill acquisition, performance signal, and professional trajectory.

Program Outcomes:

  • 10+ production-grade AI systems delivered throughout the fellowship
  • Immediate placement into federal engineering positions (GS-12 level, ~$160K–$200K+ based on background + full benefits)
  • Contribution to mission-critical systems that shape how the U.S. government designs and deploys technology
  • Access to a network of AI-native engineers working at the leading edge of public sector technology

If you are prepared to be judged on your output — not your pedigree — submit your application now.

What you will be doing

  • Deliver production-quality AI applications weekly against firm deadlines
  • Develop systems using modern AI-native approaches (agents, tool integration, evaluations, retrieval, deployment pipelines)
  • Engage and compete with elite engineering peers in a feedback-rich setting
  • Solve real, loosely defined problems reflective of government and enterprise contexts
  • Convert actual project briefs into well-scoped, dependable, production-ready systems

What you will NOT be doing

  • Attending abstract coursework or listening to passive instruction — every session is oriented toward building and deploying
  • Waiting extended periods to see your work go live — you will release functional systems each week
  • Depending on academic background, credentials, or interview scores to secure your role — production results are the sole evaluation standard
  • Operating in a risk-free practice environment — the systems you create will meet authentic security and reliability requirements

Key responsibilities

Deliver production-quality AI systems under authentic operational constraints that validate readiness for federal engineering positions.

Candidate requirements

  • U.S. citizenship required (no exceptions; background check required)
  • Demonstrated engineering ability (new grads and experienced engineers considered)
  • Willing to relocate to Austin, TX for 7 weeks (full-time, in person)
  • Willing to relocate to the Washington, DC area upon program completion (no remote roles)
  • Strong problem-solving ability, learning speed, and clear reasoning under pressure
  • High responsiveness to feedback and ability to operate in high-intensity environments

Meet a successful candidate

Watch Interview
Fabiano Lucchese
Fabiano  |  SVP of Software Engineering
Brazil

Does your company encourage your natural creativity? This Brazilian engineering leader rediscovered his purpose after unleashing both his an...

Meet Fabiano

Applying for a role? Here’s what to expect.

Crossover's skill assessment process combines innovative AI power with decades of human research, to take the guesswork, human bias, and pointless filters out of recruiting high-performing teams.

Chat-style
screening interview.
STEP 1

Chat-style
screening interview.

Cognitive 
aptitude test.
STEP 2

Cognitive 
aptitude test.

Prove real-world 
job skills.
STEP 3

Prove real-world 
job skills.

Interview with the hiring manager.
STEP 4

Interview with the hiring manager.

Pass
proctored test.
STEP 5

Pass
proctored test.

Accept job offer.
STEP 6

Accept job offer.

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About Crossover

What you will learn

Phase 1: Remote (Weeks 1–3) — Foundations in AI-First Engineering

  • AI-native development practices (coding agents, MCP, collaborative workflows in real time)
  • Retrieval-Augmented Generation (RAG), embeddings, and vector database systems
  • Accelerated project cycles emphasizing delivery under constraints

Phase 2: Onsite in Austin (Weeks 4–10) — Scalable Production AI

  • Agent architectures, evaluation frameworks, verification, and observability tooling (LangChain/LangSmith/LangFuse/CrewAI)
  • Enterprise-level execution: quality assurance, reliability engineering, and high-bar delivery standards
  • Fine-tuning and deployment strategies (LoRA/QLoRA + production-grade integration)
  • Multi-agent approaches to legacy codebase modernization
  • Multimodal AI implementations (image/video/voice) and scalable cloud infrastructure (AWS/Azure)

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Why Crossover

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The Olympics of work

It’s super hard to qualify—extreme quality standards ensure every single team member is at the top of their game.

Premium pay for premium talent

Premium pay for premium talent

Over 50% of new hires double or triple their previous pay. Why? Because that’s what the best person in the world is worth.

Shortlist by skills, not bias

Shortlist by skills, not bias

We don’t care where you went to school, what color your hair is, or whether we can pronounce your name. Just prove you’ve got the skills.

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